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  • 1.
    Aslam, Tehseen
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis2014In: International Journal of Manufacturing Research, ISSN 1750-0605, Vol. 9, no 1, p. 27-57Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to address the dilemma of supply chain management (SCM) within a truly Pareto-based multi-objective context. This is done by introducing an integration of system dynamics and multi-objective optimisation. An extended version of the well-known pedagogical SCMproblem, the Beer Game, originally developed at MIT since the 1960s, has been used as the illustrative example. As will be discussed in the paper, the integrated multi-objective optimisation and system dynamics model has been shown to be very useful for revealing how the parameters in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimisation of inventory cost, backlog cost, and the bullwhip effect.

  • 2.
    Aslam, Tehseen
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Integrating System Dynamics and Multi-Objective Optimization for Manufacturing Supply Chain Analysis2012In: Proceedings of the 5th Swedish Production symposium (SPS'12), 2012, p. 433-441Conference paper (Refereed)
    Abstract [en]

    The aim of this paper is to address the dilemma of Supply Chain Management (SCM) within a truly Pareto-based multi-objective context. This is done by introducing an integration of System Dynamics and Multi-Objective Optimization. Specifically, the paper contrasts local optimization with global optimization for SCM in which optimal trade-off solutions in the entity level, i.e. optimizing the supply chain from the perspectives of individual (local) entities. e.g., supplier, factory, distributor and retailer, are collected and compared to those obtained from an overall supply chain level (global) optimization. An extended version of the well-known pedagogical SCM problem, the Beer Game, originally developed at MIT since the 1960s, has been used as the illustrative example. As will be discussed in the paper, the integrated multi-objective optimization and system dynamics model has been shown to be very useful for revealing that how the parameters in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect.

  • 3.
    Karlsson, Ingemar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    An interactive decision support system using simulation-based optimization and knowledge extraction2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The use of simulation to improve existing manufacturing systems is not new, but simulation can also be used increase the understanding of production systems that have not yet been built. The power of simulation models can be further enhanced by using simulation-based optimization, in which an optimization algorithm tries to find optimal solutions, given certain objectives. However, extracting knowledge from the data resulting from simulation experiments and simulation-based optimization is a complex task. Therefore, tools are needed to assist users in this task. These tools can be visual, like diagrams, or can be generated by data mining. The process of running a study using simulation-based optimization to extract knowledge is a manual task that can in part be automated using existing tools, but to the author’s knowledge there is no software that implements the complete process. This work aims to develop a novel decision support system to support the generic decision process when using simulation and simulation-based optimization. The first step in setting up such a system is to understand how industry currently uses simulation and simulation-based optimization in manufacturing operations. Thus a questionnaire was distributed to manufacturing companies and organizations. The results showed that these techniques are being used, but that companies want more help with the analysis of the results as well as an automated guide in the decision process. This work proposes a system that supports a generic decision process by providing a tool with which a user can define a workflow in their organization, using simulation-based optimization as one component. The decision support system then provides tools for extracting knowledge in the form of diagrams and performs data mining for automated analysis. Data mining is part of the workflow as a tool for extracting knowledge after an optimization, as well as a tool for guiding optimization to suit the users’ preferences. The decision support system also provides for visualization of simulation models and optimization results using augmented reality. A head-mounted display helps users to see the results and model behaviors in 3D. This technology also makes it possible for users to collaborate, both in the same location and remotely. These visual and automatic analysis tools are shown to be effective in several application studies of real-world production scenarios in which data mining has been used to extract important knowledge that would be hard to obtain manually. Together with the automated workflow and efficient visualization of simulation and optimization results in augmented reality, the decision support system is believed to be an effective tool for extracting knowledge for general production systems design and analysis.

  • 4.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Combining augmented reality and simulation-based optimization for decision support in manufacturing2017In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3988-3999Conference paper (Refereed)
    Abstract [en]

    Although the idea of using Augmented Reality and simulation within manufacturing is not a new one, the improvement of hardware enhances the emergence of new areas. For manufacturing organizations, simulation is an important tool used to analyze and understand their manufacturing systems; however, simulation models can be complex. Nonetheless, using Augmented Reality to display the simulation results and analysis can increase the understanding of the model and the modeled system. This paper introduces a decision support system, IDSS-AR, which uses simulation and Augmented Reality to show a simulation model in 3D. The decision support system uses Microsoft HoloLens, which is a head-worn hardware for Augmented Reality. A prototype of IDSS-AR has been evaluated with a simulation model depicting a real manufacturing system on which a bottleneck detection method has been applied. The bottleneck information is shown on the simulation model, increasing the possibility of realizing interactions between the bottlenecks. 

  • 5.
    Karlsson, Ingemar
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Aslam, Tehseen
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Dudas, Catarina
    Volvo Group Trucks Operations.
    An Interactive, Cloud-Based Simulation Optimization System for Knowledge Discovery and Decision Support In Manufacturing2014In: Proceedings of the sixth Swedish Production Symposium, 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Designing or improving a manufacturing system involves a series of complex decisions over time to satisfy the strategic objectives of the company. To select the optimal parameters of the system entities so as to achieve the desired overall performance of the system is a very complex task that has been proven to be difficult, even for a seasoned decision maker. One of the major barriers for more efficient decision making in manufacturing is that whilst there is in principle abundant data from various levels of the factory, these data need to be organized and transferred into knowledge suitable for decision-making support. The integration of decision-making support and knowledge management has been identified to be more and more important in both scientific research and from industrial companies. The concept of deciphering knowledge from multi-objective optimization was first proposed by Deb with the term innovization (innovation via optimization). By integrating the concept of innovization with simulation, a new set of powerful tools for manufacturing systems analysis, in order to support optimal decision making in design and improvement activities, is emerged. This method is so-called Simulation-based Innovization (SBI), which has been proven to produce promising results in our previous application studies. Nevertheless, to promote the wider use of such a new method requires the development of an integrated software toolset. The goal of this paper is therefore to outline a Cloud-computing based system architecture for implementing such a SBI-based Interactive Decision Support System.

  • 6.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    An interactive decision support system using simulation-based optimization and data mining2015In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, IEEE Press, 2015, p. 2112-2123Conference paper (Refereed)
    Abstract [en]

    This paper describes a decision support system (DSS) built on knowledge extraction using simulation-based optimization and data mining. The paper starts with a requirements analysis based on a survey conducted with a number of industrial companies about their practices of using simulations for decision support.Based upon the analysis, a new, interactive DSS that can fulfill the industrial requirements, is proposed.The design of the cloud-based system architecture of the DSS is then described. To show the functionality and potential of the proposed DSS, an application study has been performed for the optimal design of a hypothetical but realistic flexible production cell. How important knowledge with respect to different preferences of the decision maker can be generated as rules, using the new Flexible Pattern Mining algorithm provided in the DSS, will be revealed by the results of this application study.

  • 7.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Interactive and Intelligent Decision Support in Manufacturing using Simulation Based Innovization and Cloud Computing2014In: Industrial Simulation Conference, Skövde, June 11-13, 2014, 2014, p. 69-74Conference paper (Refereed)
    Abstract [en]

    Simulation-based innovization is a method for extracting knowledge from a simulation model and optimization. This method can help decision makers to make high-quality decisions for their manufacturing systems so as to enhance the competitiveness of companies. Nevertheless, the simulation-based innovization process can be computationally costly and having these resources in-house can be expensive. By running the process in a cloud environment instead, the company only pays for the resources they are using. This paper proposes the concept of a cloud-based computing platform that can run the simulation-based innovization process and discuss its possibilities and challenges.

  • 8.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Gothenburg, Sweden.
    Karlsson, Ingemar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Towards Automated Multi-Objective Rule Extraction2016In: Proceedings of the 2016 European Simulation and Modelling Conference / [ed] José Évora-Gómez & José Juan Hernandéz-Cabrera, EUROSIS - The European Multidisciplinary Society for Modelling and Simulation Technology , 2016, p. 64-68Conference paper (Refereed)
  • 9.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Grimm, Henrik
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Andersson, Martin
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Simulation-Based Optimization of a Complex Mail Transportation Network2008In: Proceedings of the 2008 Winter Simulation Conference / [ed] S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler, New York: IEEE conference proceedings, 2008, p. 2625-2631Conference paper (Refereed)
    Abstract [en]

    The Swedish Postal Services receives and distributes over 22 million pieces of mail every day. Mail transportation takes place overnight by airplanes, trains, trucks, and cars in a transportation network comprising a huge number of possible routes. For testing and analysis of different transport solutions, a discrete-event simulation model of the transportation network has been developed. This paper describes the optimization of transport solutions using evolutionary algorithms coupled with the simulation model. The vast transportation network in combination with a large number of possible transportation configurations and conflicting optimization criteria make the optimization problem very challenging. A large number of simulation evaluations are needed before an acceptable solution is found, making the computational cost of the problem severe. To address this problem, a computationally cheap surrogate model is used to offload the optimization process.

  • 10.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    An Industrial Case Study of Web-based Simulation-Optimization2011In: Proceedings of the 9th Industrial Simulation Conference, Eurosis , 2011, p. 115-120Conference paper (Refereed)
    Abstract [en]

    This paper presents a web-based simulation-optimization system for improving production schedules in an advanced manufacturing cell at Volvo Aero Corporation in Sweden. The optimization aims at prioritizing components being processed in the cell in a way that minimizes both tardiness and lead times. Results from evaluating the implemented system shows a great improvement potential, but also indicates that further development is necessary before the system can be taken into operation.

  • 11.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Svantesson, Joakim
    Volvo Aero Corporation, SE- 461 81 Trollhättan, Sweden.
    Almgren, Torgny
    Volvo Aero Corporation, SE- 461 81 Trollhättan, Sweden.
    A web-based platform for the simulation-optimization of industrial problems2013In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 64, no 4, p. 987-998Article in journal (Refereed)
    Abstract [en]

    This study presents a platform for industrial, real-world simulation-optimization based on web techniques. The design of the platform is intended to be generic and thereby make it possible to apply the platform in various problem domains. In the implementation of the platform, modern web techniques, such as Ajax, JavaScript, GWT, and ProtoBuf, are used. The platform is tested and evaluated on a real industrial problem of production optimization at Volvo Aero Corporation, a company that develops and manufactures high-technology components for aircraft and gas turbine engines. The results of the evaluation show that while the platform has several benefits, implementing a web-based system is not completely straightforward. At the end of the paper, possible pitfalls are discussed and some recommendations for future implementations are outlined. (C) 2013 Elsevier Ltd. All rights reserved.

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